The present disclosure relates to a learning apparatus and method, an image processing apparatus and method, a program, and a recording medium, and particularly, to a learning apparatus and method, an image processing apparatus and method, a program, and a recording medium that enable a high-accuracy resolution interpolation to be performed at a high speed.
In the past, high-quality image processing has been put to practical use. For example, when resolution of an input signal does not satisfy resolution of a display screen, super-resolution technology that is used as resolution interpolation technology for compensating for the resolution is known. If the super-resolution technology is used, insufficient pixels can be interposed and more real video can be enjoyed, when video software having standard resolution is viewed using a full-HD wide screen television.
The super-resolution technology of related art estimates pixels of a high-resolution image from a low-resolution image by a repeated operation and generally executes the following processing.
First, a camera model (deterioration model) and camera movement (aligning) are estimated in advance. In addition, a high-resolution image is corrected gradually by a repeated operation such that an error (difference) of an estimated low-resolution image obtained from the high-resolution image through an estimation model and an actually observed low-resolution image decreases, to become similar to an ideal image.
For example, in the camera model (deterioration model), blur (optical blur, motion blur, and PSF), pixel number conversion (down conversion and progressive interlace conversion), and noise are considered. In the aligning, an estimation of camera movement or object movement with sub-pixel accuracy is used. When the camera model and the aligning are accurately known, a high-resolution image that has no aliasing can be restored.
In addition, technology for adding a feedback value calculated by a super-resolution processor to a high-resolution image stored in a buffer, adding a high-resolution image obtained by first addition processing to a feedback value obtained by a next super-resolution processor, and generating a high-resolution image by super-resolution processing using a Gauss-Seidel method is suggested (for example, refer to Japanese Patent Application Laid-open No. 2008-140012).